A Mobile Edge Computing Architecture for Safety in Mining Industry

Fang, Liangcai; Ge, Chungui; Zu, Guolin; Wang, Xinkun; Ding, Weiguo; Xiao, Changliang; Zhao, Liang

doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00269

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…Edge platforms can play a critical role in enabling the connectivity of different machinery, equipment, and personnel in the environment [28]. For this, irst, a safety zone should be de ined, and then, location technologies need to be embedded into user equipment and machinery [29].…”

Section: Safety Zones and Alertingmentioning

confidence: 99%

Edge computing for critical environments: Vision and existing solutions

Ijaz Ahmad,

Andrea Gentili,

Rupender Singh

et al. 2023

ITU J-FET

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With the increasing connectivity of critical infrastructures through wireless technologies, the importance of edge computing is increasing manyfolds. Edge computing has become an important phenomenon combining the strengths of distributed computing technologies with those of telecommunication technologies. Therefore, new technological breakthroughs are happening in the realm of edge computing for critical environments, such as the next generation underground and open-pit mining. Since the mining technologies are moving at a faster pace towards digitization leveraging connectivity technologies, edge computing plays a crucial role in bringing computing and connectivity into mines to provide latency- and security-critical services on site. In this article, we study how edge computing fulfills the needs of critical environments, focusing on mining environments, and provides important insight into future research directions.

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Section: Safety Zones and Alertingmentioning

confidence: 99%

Edge computing for critical environments: Vision and existing solutions

Ijaz Ahmad,

Andrea Gentili,

Rupender Singh

et al. 2023

ITU J-FET

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“…The mine edge devices are deployed industrially to constitute the underground ring network, and each edge node communicates with each other to display all kinds of information collected through the edge data collection gateway, through the monitoring terminal and the ground base station and, finally, through the network in the scheduling center of the monitoring platform [14][15][16], as shown in Figure 1. For different scenarios and different emergency response levels, the information collected by the devices needs to be implemented at the edge gateway for task offloading [17,18]; according to this detection method, it can ensure the efficiency of mine production tasks, improve production safety, ensure the effective utilization of mine edge resources, and reduce task processing time delay. Therefore, edge architecture and improved target detection algorithms need to be designed to ensure the effectiveness of real-time mine monitoring and network utilization in edge nodes, and so improve monitoring task efficiency and reduce task resource occupancy.…”

Section: Overall Architecturementioning

confidence: 99%

ECViST: Mine Intelligent Monitoring Based on Edge Computing and Vision Swin Transformer-YOLOv5

et al. 2022

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Mine video surveillance has a key role in ensuring the production safety of intelligent mining. However, existing mine intelligent monitoring technology mainly processes the video data in the cloud, which has problems, such as network congestion, large memory consumption, and untimely response to regional emergencies. In this paper, we address these limitations by utilizing the edge-cloud collaborative optimization framework. First, we obtained a coarse model using the edge-cloud collaborative architecture and updated this to realize the continuous improvement of the detection model. Second, we further proposed a target detection model based on the Vision Swin Transformer-YOLOv5(ViST-YOLOv5) algorithm and improved the model for edge device deployment. The experimental results showed that the object detection model based on ViST-YOLOv5, with a model size of only 27.057 MB, improved the average detection accuracy is by 25% compared to the state-of-the-art model, which makes it suitable for edge-end deployment in mining workface. For the actual mine surveillance video, the edge-cloud collaborative architecture can achieve better performance and robustness in typical application scenarios, such as weak lighting and occlusion, which verifies the feasibility of the designed architecture.

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“…With the development of artificial intelligence, IoT, digital twin [69], and parallel intelligence [70], the manufacturing industry is moving towards the goal of smart manufacturing. A number of edge computing frameworks or applications based on virtualization technologies are deployed to different industrial processes, e.g., semiconductor manufacturing [71], robotic assistance for emergency management [72], explosion prevention in mining industry [73], maintenance management [74,75], Fabric defect detection for textile production [76], oil and gas production [26,77], spectroscopic inspection for olive [78], and Augmented Reality for shipbuilding [79]. In this section, we focus on illustrating equipment fault diagnosis and computation of scheduling tasks.…”

Section: Applications To Industrial Processesmentioning

confidence: 99%

Evolving Container to Unikernel for Edge Computing and Applications in Process Industry

Chen

Zhou

2021

Processes

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Industry 4.0 promotes manufacturing and process industry towards digitalization and intellectualization. Edge computing can provide delay-sensitive services in industrial processes to realize intelligent production. Lightweight virtualization technology is one of the key elements of edge computing, which can implement resource management, orchestration, and isolation services without considering heterogenous hardware. It has revolutionized software development and deployment. The scope of this review paper is to present an in-depth analysis of two such technologies, Container and Unikernel, for edge computing. We discuss and compare their applicability in terms of migration, security, and orchestration for edge computing and industrial applications. We describe their performance indexes, evaluation methods and related findings. We then discuss their applications in industrial processes. To promote further research, we present some open issues and challenges to serve as a road map for both researchers and practitioners in the areas of Industry 4.0, industrial process automation, and advanced computing.

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A Mobile Edge Computing Architecture for Safety in Mining Industry

Cited by 9 publications

References 16 publications

Edge computing for critical environments: Vision and existing solutions

Edge computing for critical environments: Vision and existing solutions

ECViST: Mine Intelligent Monitoring Based on Edge Computing and Vision Swin Transformer-YOLOv5

Evolving Container to Unikernel for Edge Computing and Applications in Process Industry

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